Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Todayâ??s prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyerâ??s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is NP-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of first or second-price sealed-bid auctions and derive the optimal coordination and bidding policies.

When Are Names Similar Or the Same? Introducing the Code Names Matcher Library

Program code contains functions, variables, and data structures that are represented by names. To promote human understanding, these names should describe the role and use of the code elements they represent. But the names given by developers show high variability, reflecting the tastes of each developer, with different words used for the same meaning or the same words used for different meanings. This makes comparing names hard. A precise comparison should be based on matching identical words, but also take into account possible variations on the words (including spelling and typing errors), reordering of the words, matching between synonyms, and so on. To facilitate this we developed a library of comparison functions specifically targeted to comparing names in code. The different functions calculate the similarity between names in different ways, so a researcher can choose the one appropriate for his specific needs. All of them share an attempt to reflect human perceptions of similarity, at the possible expense of lexical matching.Comment: 20 pages. Download from https://pypi.org/project/namecompare

The Development of a Smart Map for Minimum Exertion Routing Applications

The problem of minimum cost routing has been extensively explored in a variety of contexts. While there is a prevalence of routing applications based on least distance, time, and related attributes, exertion-based routing has remained relatively unexplored. In particular, the network structures traditionally used to construct minimum cost paths are not suited to representing exertion or finding paths of least exertion based on road gradient. In this paper, we introduce a topographical network or “topograph” that enables minimum cost routing based on the exertion metric on each arc in a given road network as it is related to changes in road gradient. We describe an algorithm for topograph construction and present the implementation of the topograph on a road network of the state of California with ~22 million nodes

Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Today’s prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer’s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is N P-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of firstor second-price sealed-bid auctions and derive the optimal coordination and bidding policies

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach

Elicitation of strategies in four variants of a round-robin tournament: the case of Goofspiel

Goofspiel is a simple two-person zero-sum game for which there exist no known equilibrium strategies. To gain insight into what constitute winning strategies, we conducted a round-robin tournament in which participants were asked to provide computerized programs for playing the game with or without carryover. Each of these two variants was to be played under two quite different objective functions, namely, maximization of the cumulative number of points won across all opponents (as in Axelrod's tournament), and maximization of the probability of winning any given round. Our results show that there are, indeed, inherent differences in the results with respect to the complexity of the game and its objective function, and that winning strategies exhibit a level of sophistication, depth, and balance that are not captured by present models of adaptive learning

Redistribution of Self-service Electric Cars: A Case of Pickup and Delivery

Projet PRAXITELEFor a large urban area an alternative to public transportation and private cars is considered in the form of a "free" use of electric cars. Such "free" use of electric cars would require periodic redistribution of the cars among different dropoff/pickup stations by a fleet of finite capacity towtrucks stationed at the various depots on the road network. In this study we examine in detail the electric car redistribution problem. The redistribution activity is modelled as that of less than truck load, pickup and delivery with nonsimple paths, allowing for split pickups and deliveries. We propose a Mixed Integer Programming formulation of the problem and we test several solution which include constraint Programming, Lagrangian relaxation methodology applied to the MIP model, and an AI solution approach with a modified A* heuristic. The solution procedures here are designed to solve large practical instances of the car redistribution problem

Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar

We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit and 10 receive antenna elements. We use frequency division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and apply the Xampling framework for signal recovery. The prototype also implements a cognitive transmission scheme where each transmit waveform is restricted to those pre-determined subbands of the full signal bandwidth that the receiver samples and processes. Real-time experiments show reasonable recovery performance while operating as a 4x5 thinned random array wherein the combined spatial and spectral sampling factor reduction is 87.5% of that of a filled 8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) 201